A random effects sensitivity analysis for patient pathways model

In this paper, we present a random effects approach to modelling of patient pathways with an application to the neonatal unit of a large metropolitan hospital. This approach could be used to identify pathways such as those resulting in high probabilities of death/survival, and to estimate cost of care or length of stay. Patient-specific discharge probabilities could also be predicted as a function of the random effect. We also investigate the sensitivity of our modelling results to random effects distribution assumptions

Modelling and performance measure of a perinatal network centre in the United Kingdom

The main aim of this paper is to model the neonatal unit of a perinatal network centre using the general framework of a loss network model and to estimate some performance measures. A special case of the class of model has been applied for capacity planning to the perinatal network centre of a neonatal network in the United Kingdom. Using the data supplied from the perinatal network centre about admission process, length of stay (LoS) and discharge pattern of the babies, the loss network model is applied to estimate the admission refusal probability in the system under steady-state conditions. Results are derived for different arrival patterns and different combinations of cots at all levels of care of the neonatal unit. This approach can be useful to select the optimal combination of cots for any given acceptance rate of arrival to the neonatal unit

Design and implementation of a deep recurrent model for prediction of readmission in urgent care using electronic health records

There has been a steady growth in machine learning research in healthcare, however, progress is difficult to measure because of the use of different cohorts, task definitions and input variables. To take the advantage of the availability and value of digital health data, we aim to predict unplanned readmissions to the intensive care unit (ICU) from a publicly available Critical Care dataset called Medical Information Mart for Intensive Care (MIMIC-III). In this research, we formulate a heterogeneous LSTM and CNN architecture specifically to create a model of readmission risk. Our proposed predictive framework outperformed all the benchmark classifiers such as support vector machine, random forest and logistic regression models on all performance measures (AUC, accuracy and precision) except on recall where random forest performed slightly better. Predictions from these models will help in resource planning and decrease mortality or length of stay in clinical care settings

Discovering Business Processes in CRM Systems by leveraging unstructured text data

Recent research has proven the feasibility of using Process Mining algorithms to discover business processes from event logs of structured data. However, many IT systems also store a considerable amount of unstructured data. Customer Relationship Management (CRM) Systems typically store information about interactions with customers, such as emails, phone calls, meetings, etc. These activities are characteristically made up of unstructured data, such as a free text subject and description of the interaction, but only limited structured data is available to classify them. This poses a problem to the traditional Process Mining approach that relies on an event log made up of clearly categorised activities. This paper proposes an original framework to mine processes from CRM data, by leveraging the unstructured part of the data. This method uses Latent Dirichlet Allocation (LDA), an unsupervised machine learning technique, to automatically detect and assign labels to activities. This framework does not require any human intervention. A case study with real-world CRM data validates the feasibility of our approach

A semi-open queueing network approach to the analysis of patient flow in healthcare systems

In this paper, we present a modelling framework for patient flow in a healthcare system using semi-open queueing network models, which introduces a total bed constraint, above which new patients will be refused admission. Hence this model provides a realistic representation of a real system. This approach enables us to have access to a range of established methods that deals with queueing network models. We demonstrate the usefulness of the model in the context of a geriatric department and show that hospital managers can use this model to gain better understanding of the dynamics of patient flow and to study potential long-term impacts of policy changes

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling

Comparative analysis of clustering-based remaining-time predictive process monitoring approaches

Predictive process monitoring aims to accurately predict a variable of interest (e.g. remaining time) or the future state of the process instance (e.g. outcome or next step). Various studies have been explored to develop models with greater predictive power. However, comparing the various studies is difficult as different datasets, parameters and evaluation measures have been used. This paper seeks to address this problem with a focus on studies that adopt a clustering-based approach to predict the remaining time to the end of the process instance. A systematic literature review is undertaken to identify existing studies that adopt a clustering-based remaining-time predictive process monitoring approach and performs a comparative analysis to compare and benchmark the output of the identified studies using five real-life event logs

A tool for studying the effects of residents' attributes on patterns of length of stay in long-term care

Understanding the differential pattern of length of stay (LOS) in long-term care (LTC) due to residents' attributes has important practical implications in the management of long-term care. In this paper, we extend a previously developed modelling approach to incorporate residents' attributes. Two applications using data collected by a local authority in England are presented to demonstrate the potential use of this extension. In the study of possible difference in LOS pattern due to gender, our model provides quantitative support to the observations that male residents admitted to NC take more time to settle down and have poorer short-term survival prospect than female residents

Modeling Patient Flows: A Temporal Logic Approach

Constructing a consistent process model can be instrumental in streamlining healthcare issues. Current process modeling techniques used in healthcare, such as flowcharts, unified modeling language activity diagram (UML AD), and business process modeling notation (BPMN) are intuitive and imprecise. These techniques are vague in process description and cannot fully capture the complexities of the types of activities and full extent of temporal constraints between them. Additionally, to schedule patient flows, current modeling techniques do not offer any mechanism, so healthcare relies on critical path method (CPM) and program evaluation review technique (PERT), that also have limitations i.e. finish-start barrier. It is imperative that temporal constraints between the start and/or end of a process needs to be specified, e.g., the start of A precedes the start (or end) of B, etc., however, these approaches failed to provide us with a mechanism for handling these temporal situations. This paper proposes a framework that provides enumeration of core terms/concepts to describe a general knowledge basis for Business and Healthcare domains. Definitions are provided to present the semantics of concepts i.e. based on their ontology. Furthermore, this logical basis is supported by Point graph (PG) notation; a graphical tool, which has a formal translation to a point interval temporal logic (PITL), and is used to model Patient flows suitable for enhanced reasoning and correct representation. We will evaluate an illustrative discharge patient flow example initially modeled using Unified Modeling Language Activity Diagram (UML AD) with the intention to compare with the technique presented here for its potential use to model patient flows